Method and system for surface wear inspection using millimeter wave radar

Abstract

The present disclosure provides a method for surface wear inspection using millimeter wave radar. The system initially receives a plurality of uncompressed raw Synthetic Aperture Radar (SAR) images. Further, a plurality of reconstructed SAR images are generated based on the plurality of uncompressed raw SAR images using a variable focusing based Range Doppler Algorithm (RDA). Further, a master image and a slave image are selected from the reconstructed SAR images and corresponding anchor points are assigned. Further a plurality of fine level and coarse level shift coordinates are computed based on the corresponding anchor points. Further, a net shift value is computed based on the plurality of fine level and coarse level shift coordinates. The master and the slave images are aligned based on the net shift value and the interferogram is generated. The interferogram is further analyzed to profile the corresponding deformation pertaining to the surface under test.

Description

TECHNICAL FIELD

[0001] The disclosure herein generally relates to the field of interferometry and, more particularly, to a method and system for surface wear inspection using millimeter wave radar.BACKGROUND

[0002] In tribological systems, surface wear is referred to as any change to the surface that involves progressive modification of the surface or loss / deposition of material. The surface wear results in plastic dislodging of surface / near-surface materials on micrometer to millimeter scale. The abrasive wear caused in milling and grinding process can wear out the protective lining of the mill equipment. Eventually, the mill must be stopped for repair and maintenance, causing undesirable downtime cost of the mill. A solution to this problem is predictive maintenance using online sensors that actively profile the surface deformation. The dust that is generated due to milling and grinding process cripples existing sensor modalities like laser surface profilers which cannot see through dust. This brings the millimeter (mm)-wave imaging systems like Synthetic Aperture Radar (SAR) into the picture, which can penetrate through occluded media while preserving the high resolution.

[0003] Conventional methods related to Interferometric Synthetic Aperture Radar (InSAR) are mostly centered around ground displacement measurements using space-borne, air-borne, or Ground Based (GB)-InSAR. Even GB-InSAR used at medium range have per-pixel resolution in meters which is not sufficient for imaging wear and small surface defects. Since existing literature on surface profiling techniques indicates a gap in in-situ and unobtrusive measurement of wear with sub-millimeter accuracy during operation, especially using radars. there is a challenge in addressing in-situ surface wear examination based on high resolution short-range InSAR images.

[0004] CN 113 340 191 A discloses a deformation quantity measuring method of a time series interference SAR and an SAR system. The deformation amount measuring method includes: carrying out data preprocessing on the SAR image set; removing flat ground and terrain phases from the interference phases to generate differential interference phases, and calculating pixel by pixel to generate a differential interference pattern; carrying out linear deformation phase estimation of time and space domains on the differential interference phase to obtain a time sequence deformation phase of each point target; and calculating phase transformation deformation according to the radar wavelength parameters, thereby obtaining the deformation measurement value of the SAR. The method has the advantages of high deformation measurement precision, concise and efficient calculation steps and stable calculation result.

[0005] GUERRIERO ANDREA ET AL: "Efficient implementation of InSAR time-consuming algorithm kernels on GPU environment" discloses satellite remote sensing radar technologies provide powerful tools for geohazard monitoring and risk management at synoptic scale. In particular, advanced Multi-Temporal SAR Interferometric algorithms are capable to detect ground deformations and structural instabilities with millimetric precision, but impose strong requirements in terms of hardware re-sources. Recent advances in GPU computing and programming hold promise for time efficient implementation of imaging algorithms, thus enhancing the development of advanced Emergency Management Services based on Earth Observation technologies. In this study, a preliminary assessment of the potentials of GPU processing is carried out, by comparing CPU (single- and multi-thread) and GPU implementations of InSAR time-consuming algorithm kernels. In particular, it is focused on the fine coregistration of SAR interferometric pairs, a crucial step in the interferogram generation process.

[0006] GOMBA GIORGIO ET AL: "Toward Operational Compensation of Ionospheric Effects in SAR Interferograms: The Split-Spectrum Method" discloses the differential ionospheric path delay is a major error source in L-band interferograms. It is superimposed to topography and ground deformation signals, hindering the measurement of geophysical processes. Proceed toward the realization of an operational processor to compensate the ionospheric effects in interferograms. The processor should be robust and accurate to meet the scientific requirements for the measurement of geophysical processes, and it should be applicable on a global scale. An implementation of the split-spectrum method, which will be one element of the processor, is presented in detail, and its performance is analyzed. The method is based on the dispersive nature of the ionosphere and separates the ionospheric component of the interferometric phase from the nondispersive component related to topography, ground motion, and tropospheric path delay. Tested the method using various Advanced Land Observing Satellite Phased-Array type L-band synthetic aperture radar interferometric pairs with different characteristics: high to low coherence, moving and nonmoving terrains, with and without topography, and different ionosphere states. Ionospheric errors of almost 1 m have been corrected to a centimeter or a millimeter level. The results show how the method is able to systematically compensate the ionospheric phase in interferograms, with the expected accuracy, and can therefore be a valid element of the operational processor.

[0007] ZHANG LEI ET AL: "Motion compensation using substrip PGA" discloses MOtion COmpensation (MOCO) is an essential step in high resolution airborne Synthetic Aperture Radar (SAR) imaging. Generally, a reference altitude level is assumed and external Digital Elevation Model (DEM) is required for the scene topography heavily varied. To overcome the shortcoming, we propose a MOCO method based on Phase Gradient Autofocus (PGA) which can obtain well focused images without DEM. In the implementation, we first compensate the normal rangeinvariant term. Then the data are divided into strips in range-compressed domain and PGA is applied to each substrip to extract the phase errors. Finally, the phase error surface is obtained using interpolation and then compensated. Real airborne SAR data of a UAV-SAR system experiments and comparisons demonstrate the validity and effectiveness of the proposed algorithm.SUMMARY

[0008] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. In an aspect, a processor implemented method for surface wear inspection of metallic surfaces using a short range Interferometric Synthetic Aperture Radar technique according to appended claims 1-3 is provided.

[0009] In another aspect, a system for surface wear inspection of metallic surfaces using a short range Interferometric Synthetic Aperture Radar technique according to appended claims 4-6 is provided.

[0010] In yet another aspect, a computer program product including a non-transitory computer-readable medium having embodied therein a computer program for surface wear inspection of metallic surfaces using a short range Interferometric Synthetic Aperture Radar technique according to appended claim 7 is provided.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles: FIG. 1 is a functional block diagram of a system for surface wear inspection using millimeter wave radar, in accordance with some embodiments of the present disclosure. FIG. 2 illustrates a functional architecture of the system of FIG. 1, for surface wear inspection using millimeter wave radar, in accordance with some embodiments of the present disclosure. FIGS. 3A through 3C are an exemplary flow diagrams illustrating a processor implemented method 300 for surface wear inspection using millimeter wave radar, implemented by the system of FIG. 1 according to some embodiments of the present disclosure. FIG. 4A illustrates a repeat pass SAR interferometry for profiling wear on a sample, implemented by the system of FIG. 1 according to some embodiments of the present disclosure. FIG. 4B illustrates an imaging geometry for a side looking Synthetic Aperture Radar (SAR) setup for surface wear inspection using millimeter wave radar, implemented by the system of FIG. 1 according to some embodiments of the present disclosure. FIGS. 5A and 5B are exemplary flow diagrams illustrating a processor implemented method for generating the plurality of reconstructed SAR images, implemented by the system of FIG. 1 according to some embodiments of the present disclosure. FIG. 6 illustrates example anchor points for the processor implemented method for generating the plurality of reconstructed SAR images, implemented by the system of FIG. 1 according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS

[0012] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts.

[0013] In-situ inspection of surface (for example, metallic surfaces) wear remains a daunting task for the industry. Detection of intermediate progressive dynamics of wear requires repeated examinations which is time-consuming, adding to mill downtime. Runtime inspection inside the mill necessitates the ability to sense through occluded media due to the presence of grinding dust. The present disclosure utilizes short range interferometric synthetic aperture radar (InSAR) to detect and quantify wear or other tribological phenomena associated with metallic surfaces. A novel variable focusing algorithm has been used to generate SAR images and a unique automated co-registration scheme has been employed in the InSAR processing pipeline. The reconstructed SAR images and the generated interferogram reflect strong overlap with the ground truth of the sample under test with minimum Mean Square Error (MSE).

[0014] The key contributions of the present disclosure are -1) Short range, repeat-pass InSAR technique for in-situ quantification of wear on metallic surfaces. 2) Automated, anchor point-based InSAR co-registration scheme for alignment of SAR images. 3) Novel variable focusing in range-doppler algorithm (RDA) to generate SAR images.

[0015] Embodiments herein provide a method and system for surface wear inspection using millimeter wave radar. The surface wear inspection is performed using a novel variable focusing algorithm to generate SAR images and a unique automated co-registration scheme to identify a deformation pertaining to a surface under test by generating interferograms. The system initially receives a plurality of uncompressed raw Synthetic Aperture Radar (SAR) images pertaining to a surface under test. The plurality of uncompressed raw SAR images includes a first plurality of raw SAR images captured sequentially from a plurality of vantage points in a single pass and a second plurality of raw SAR images captured sequentially from the plurality of vantage points in a repeat pass. Further, a plurality of reconstructed SAR images are generated based on the plurality of uncompressed raw SAR images using a variable focusing based Range Doppler Algorithm (RDA). Further, a master image and a slave image is selected from the reconstructed SAR images. Further, a plurality of anchor points are assigned for the master and the slave image. Further a plurality of fine level and coarse level shift coordinates are computed based on the plurality of anchor points. Further, a net shift value is computed based on the plurality of fine level and coarse level shift coordinates. The master and the slave images are aligned based on the net shift value and the interferogram is generated. The interferogram is further analyzed to profile the corresponding deformation pertaining to the surface under test.

[0016] Referring now to the drawings, and more particularly to FIGS. 1 through 6, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and / or method.

[0017] FIG. 1 is a functional block diagram of an surface wear inspection using millimeter wave radar, in accordance with some embodiments of the present disclosure. The system 100 includes or is otherwise in communication with hardware processors 102, at least one memory such as a memory 104, an I / O interface 112. The hardware processors 102, memory 104, and the Input / Output (I / O) interface 112 may be coupled by a system bus such as a system bus 108 or a similar mechanism. In an embodiment, the hardware processors 102 can be one or more hardware processors.

[0018] The I / O interface 112 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I / O interface 112 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a printer and the like. Further, the I / O interface 112 may enable the system 100 to communicate with other devices, such as web servers, and external databases.

[0019] The I / O interface 112 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the I / O interface 112 may include one or more ports for connecting several computing systems with one another or to another server computer. The I / O interface 112 may include one or more ports for connecting several devices to one another or to another server.

[0020] The one or more hardware processors 102 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, node machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more hardware processors 102 is configured to fetch and execute computer-readable instructions stored in the memory 104.

[0021] The memory 104 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and / or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory 104 includes a plurality of modules 106. The memory 104 also includes a data repository (or repository) 110 for storing data processed, received, and generated by the plurality of modules 106.

[0022] The plurality of modules 106 include programs or coded instructions that supplement applications or functions performed by the system 100 for surface wear inspection using millimeter wave radar. The plurality of modules 106, amongst other things, can include routines, programs, objects, components, and data structures, which performs particular tasks or implement particular abstract data types. The plurality of modules 106 may also be used as, signal processor(s), node machine(s), logic circuitries, and / or any other device or component that manipulates signals based on operational instructions. Further, the plurality of modules 106 can be used by hardware, by computer-readable instructions executed by the one or more hardware processors 102, or by a combination thereof. The plurality of modules 106 can include various sub-modules (not shown). The plurality of modules 106 may include computer-readable instructions that supplement applications or functions performed by the system 100 for the semantic navigation using spatial graph and trajectory history. In an embodiment, the modules 106 includes a variable focusing based SAR image generation module (shown in FIG. 2) and an anchor point based image co-registration module (shown in FIG. 2). The variable focusing based SAR image generation module includes a range compressed SAR image generation module (shown in FIG. 2), a target range finding module (shown in FIG. 2), an anchor point generation module (shown in FIG. 2), a windows creation module (shown in FIG. 2), a windows parameter computation module (shown in FIG. 2), an auto-focused SAR images selection module (shown in FIG. 2), a high resolution SAR image generation module (shown in FIG. 2), a reconstructed SAR image generation module (shown in FIG. 2). The anchor point based image co-registration module includes an image selection module (shown in FIG. 2), an anchor point allocation module (shown in FIG. 2), a coarse level shift coordinates computation module (shown in FIG. 2), a pixel group generation module (shown in FIG. 2), a Fast Fourier Transform (FFT) module (shown in FIG. 2), an oversampled pixel group generation module (shown in FIG. 2), an Inverse Fast Fourier Transform (IFFT) module (shown in FIG. 2), a deformation information identification module (shown in FIG. 2), a target pixel group identification module (shown in FIG. 2), a fine level shift coordinates computation module (shown in FIG. 2), a net shift estimation module (shown in FIG. 2), an interferogram generation module (shown in FIG. 2) and a deformation profiling module (shown in FIG. 2). In an embodiment, FIG. 2 illustrates a functional architecture of the system of FIG. 1, for surface wear inspection using millimeter wave radar, in accordance with some embodiments of the present disclosure.

[0023] The data repository (or repository) 110 may include a plurality of abstracted piece of code for refinement and data that is processed, received, or generated as a result of the execution of the plurality of modules in the module(s) 106.

[0024] Although the data repository 110 is shown internal to the system 100, it will be noted that, in alternate embodiments, the data repository 110 can also be implemented external to the system 100, where the data repository 110 may be stored within a database (repository 110) communicatively coupled to the system 100. The data contained within such external database may be periodically updated. For example, new data may be added into the database (not shown in FIG. 1) and / or existing data may be modified and / or non-useful data may be deleted from the database. In one example, the data may be stored in an external system, such as a Lightweight Directory Access Protocol (LDAP) directory and a Relational Database Management System (RDBMS).

[0025] FIGS. 3A through 3C are flow diagrams illustrating a method 300 for surface wear inspection using millimeter wave radar implemented by the system of FIG. 1 according to embodiments of the present disclosure. In an embodiment, the system 100 includes one or more data storage devices or the memory 104 operatively coupled to the one or more hardware processor(s) 102 and is configured to store instructions for execution of steps of the method 300 by the one or more hardware processors 102. The steps of the method 300 of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG. 1 and the steps of flow diagram as depicted in FIGS. 3A through 3C. The method 300 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 300 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network.

[0026] At step 302 of the method 300, the one or more hardware processors 102 are configured by the programmed instructions to receive the plurality of uncompressed raw Synthetic Aperture Radar (SAR) images pertaining to a surface under test. The plurality of uncompressed raw SAR images comprises a first plurality of raw SAR images captured sequentially from a plurality of vantage points in a single pass and a second plurality of raw SAR images captured sequentially from the plurality of vantage points in a repeat pass. The single pass captures the first plurality of uncompressed raw SAR images from the plurality of vantage points at a time, and the repeat pass captures the second plurality of uncompressed raw SAR images from the plurality of vantage points at a varying time.

[0027] In an embodiment, the imaging of the surface under test can be carried out by using a contact probe based scanning mechanism or utilizing a moving platform to scan from a standoff distance using synthetic aperture radar (SAR). The SAR is a type of active data collection where a sensor produces its own energy and then records the amount of that energy reflected back after interacting with the surface under test. The incident mm-waves interact with the surface and induce a surface current density. Presence of any kind of defect, like surface wear or cracks, perturbs this current density and scatters the wave. Additionally, if a coherent radar is used in a SAR setup, the SAR images can be used in an interferometric SAR (InSAR) fashion.

[0028] In an embodiment, a commercial-off-the-shelf (COTS) pulsed coherent radar (PCR) has been used to conduct InSAR in short range for inspecting wears on a metallic surface. The data corresponding to the normal and worn-out sample was collected from two vantage points and was processed to produce interferograms of the target scene.

[0029] FIG. 4A illustrates a repeat pass SAR interferometry for profiling wear on a sample, implemented by the system of FIG. 1 according to some embodiments of the present disclosure. Now referring to FIG. 4, a SAR 402 is located at vantage point 1 and another SAR 404 is located at vantage point 2. A vantage point is a position affording a good view of the surface under test 406. Now referring to FIG. 4A, the surface under test is a metallic surface which is normal at tome t 1 and undergone wear at time t 2 . Now referring to equations (1) through (5), I k is the k th< pass master image, R k is the corresponding slant range, θ sk is the stochastic phase contribution due to scattering, ϕ k is the phase for the k th< pass, ΔR is the path difference between successive passes and ϕ ifg is the interferometric phase. I k = I k e i 4 πR k λ + θ s k ϕ k = ∠ I k = 2 2 π λ R k + θ s k R k = R k − 1 + Δ R I ifg = I k − 1 . I k ∗ ϕ ifg = 4 π λ Δ R

[0030] At step 304 of the method 300, the variable focusing based SAR image generation module 202 executed by one or more hardware processors 102 is configured by the programmed instructions to generate the plurality of reconstructed SAR images based on the plurality of uncompressed raw SAR images using the variable focusing based RDA.

[0031] FIG. 4B illustrates an imaging geometry for a side looking Synthetic Aperture Radar (SAR) setup for surface wear inspection using millimeter wave radar, implemented by the system of FIG. 1 according to some embodiments of the present disclosure. FIG. 4B depicts a radar traversing along a flight path with a constant velocity V, and illuminating the sample of interest. The along-track and cross-track view of the same has been provided with labelled parameters like look angle θL, beamwidth along E-Plane θE and H-Plane θH, near-slant range R1 and far-slant range R2.In an embodiment, every point on an illuminated scene of a side looking, SAR can be uniquely mapped to its time delay (fast-time) and Doppler shift as shown in FIG. 4B.

[0032] There are three key steps in RDA algorithm for SAR image formation including a range compression, range migration correction and azimuth compression. In range migration, the reference function consists of two terms namely range walk or Doppler centroid (f dc ) and range curvature or Doppler frequency rate (f r ) as depicted in Equation (6) and (7). Here, R(t) is the kinematic expression for range, R 0 is the range around which correction is done, θ is the squint angle, λ is the wavelength, V stands for the platform's velocity and ϕ(t) is the time domain phase reference function. It must be noted that for low squint angle, θ tends to zero. R t = R 0 − V Sinθt + V 2 Cos 2 θ 2 R 0 . t 2 ϕ t = e i 4 πR 0 λ . e i 2 π − 2 V λ Sinθt − 2 V 2 λ R 0 Cos 2 θ t 2 2

[0033] The method of generating the plurality of reconstructed SAR images is explained in conjunction with FIGS. 5A and 5B. Now referring to FIGS. 5A and 5B, at step 502 of the method 500, the range compressed SAR image generation module 206 executed by one or more hardware processors 102 is configured by the programmed instructions to generate the plurality of range compressed SAR images based on the plurality of uncompressed raw SAR images using a range reference function. The plurality of signals returned from a single point are compressed into a single pixel.

[0034] At step 504 of the method 500, the target range finding module 208 executed by one or more hardware processors 102 is configured by the programmed instructions to find a target range corresponding to each of the plurality of range compressed SAR images using the reference function, wherein the target range is where the range correction is to be done.

[0035] At step 506 of the method 500, the anchor point generation module 210 executed by one or more hardware processors 102 is configured by the programmed instructions to generate the plurality of anchor points corresponding to each of a plurality of target ranges based on a pulse-width at mid-reference level of a plotted range and a corresponding azimuth waveform using the spread function. The spread function represents a rectangular region in the space of observation where the object is situated. In an embodiment, the spread function is an array of four points comprising a near-range, a far-range, a near-azimuth and a far-azimuth as given in FIG. 6. Now referring to FIG. 6, cross-range is plotted in X-axis and the range is plotted in Y-axis. The points 602, 604, 606 and 608 are the anchor points associated with the spread function.

[0036] At step 508 of the method 500, the windows creation module 212 executed by one or more hardware processors 102 is configured by the programmed instructions to creating a plurality of windows in range direction based on the plurality of anchor points (shown in FIG. 6) using the signal processing technique.

[0037] At step 510 of the method 500, the windows parameter computation module 214 executed by one or more hardware processors 102 is configured by the programmed instructions to compute a plurality of window parameters for each of the plurality of windows in each range direction using the range reference function. The plurality of parameters comprises the Doppler Centroid value (f dc ), the Doppler frequency (f r ) value and a synthetic aperture length as shown in equations (6) and (7).

[0038] At step 512 of the method 500, the auto-focused SAR images selection module 216 executed by one or more hardware processors 102 is configured by the programmed instructions to select a plurality of auto-focused SAR images by computing a corrected focus magnitude corresponding to each of the plurality of windows based on the plurality of window parameters using an automatic focus enhancement technique. For example, the Doppler centroid and the Doppler frequency rate of each range window is used for automatic focus enhancement.

[0039] At step 514 of the method 500, the high resolution SAR image generation module 218 executed by one or more hardware processors 102 is configured by the programmed instructions to generate a plurality of high-resolution SAR image by performing an Azimuth match filtering followed by an IFT on the plurality of auto-focused SAR images. At step 516 of the method 500, the reconstructed SAR image generation module 220 executed by one or more hardware processors 102 is configured by the programmed instructions to generating the plurality of reconstructed SAR images based on the plurality of high-resolution SAR image using a ground range interpolation.

[0040] Referring to the method 300, the steps 306 to 330 are iteratively performed until each of the plurality of reconstructed SAR images is examined. At step 306 of the method 300, the image selection module 222 executed by the one or more hardware processors 102 is configured by the programmed instructions to select a master image from the first plurality of reconstructed SAR images and a slave image from the second plurality of reconstructed SAR images, from among the plurality of reconstructed SAR images.

[0041] At step 308 of the method 300, the anchor point allocation module 224 executed by the one or more hardware processors 102 is configured by the programmed instructions to assign the plurality of anchor points for the master image and the plurality of anchor points for the slave image using a signal processing technique, wherein the plurality of anchor points are generated using a spread function.

[0042] At step 310 of the method 300, the coarse level shift coordinates computation module 226 executed by the one or more hardware processors 102 is configured by the programmed instructions to compute a plurality of coarse level shift coordinates based on a difference between the plurality of anchor points corresponding to the master image and the plurality of anchor points corresponding to the slave image. For example, the coarse level shift coordinates are 8cm (range) and 7.16cm (azimuth)

[0043] At step 312 of the method 300, the pixel group generation module 228 executed by the one or more hardware processors 102 is configured by the programmed instructions to generate a plurality of pixel groups for the master image and a plurality of pixel groups for the slave image by extracting a pixel group surrounding each of the plurality of anchor points corresponding to the master image and the slave image using a pixel extraction technique. For example, a small pixel group of 28x28 pixels size is defined for slave image and similarly another small group of 32x32 pixels size is defined for master image. These group of pixels are selected around the corresponding anchor points.

[0044] At step 314 of the method 300, the Fast Fourier Transform (FFT) module 230 executed by the one or more hardware processors 102 is configured by the programmed instructions to generate a frequency domain representation of the plurality of pixel groups for the master image and a frequency domain representation of the plurality of pixel groups for the slave image by computing a 2D Fast Fourier Transform (FFT) on the corresponding plurality of pixel groups.

[0045] At step 316 of the method 300, the oversampled pixel group generation module 232 executed by the one or more hardware processors 102 is configured by the programmed instructions to obtain a plurality of over-sampled pixel groups for the master image and a plurality of over-sampled pixel groups for the slave image based on the corresponding frequency domain representation using zero padding. The zero padding is done in the frequency domain for oversampling. Apart from the existing number of sampling points, additional zero-valued points are taken for the oversampling operation.

[0046] At step 318 of the method 300, the IFFT module 234 executed by the one or more hardware processors 102 is configured by the programmed instructions to generate a spatial domain representation of the plurality of over-sampled pixel groups for the master image and a spatial domain representation of the plurality of over-sampled pixel groups for the slave image by computing an Inverse FFT (IFFT) on the plurality of over-sampled pixel groups corresponding to the master image and the slave image.

[0047] At step 320 of the method 300, the deformation information identification module 236 executed by the one or more hardware processors 102 is configured by the programmed instructions to identify a deformation information associated with each of the plurality of pixel groups for the master image and a deformation information associated with each of the plurality of pixel groups for the slave image based on the corresponding spatial domain representation using a spatial data analysis technique. For example, in the interferogram, the region with uniform phase represents a planar surface and the region with a interferometric-phase change represents surface deformation. In this way, wear zones are identified and exact deformation is calculated by subtracting the later from the uniform phase value.

[0048] At step 322 of the method 300, the target pixel group identification module 238 executed by the one or more hardware processors 102 is configured by the programmed instructions to identify a target pixel group for the master image and a target pixel group for the slave image based on the corresponding deformation information. In an embodiment, the pixel group having maximum deformation information is selected as the target pixel group.

[0049] At step 324 of the method 300, the fine level shift coordinates computation module 240 executed by the one or more hardware processors 102 is configured by the programmed instructions to compute a plurality of fine level shift coordinates between the master image and the slave image based on corresponding 2D cross-correlated pixel groups using a sub-pixel level spatial shift computation technique. The fine level shifting are in sub-mm level for accurate alignment. In an embodiment, the sub-pixel level spatial shift computation is achieved using oversampling techniques.

[0050] At step 326 of the method 300, the net shift estimation module 242 executed by the one or more hardware processors 102 is configured by the programmed instructions to estimate a net shift value based on the plurality of coarse level shift coordinates and the plurality of fine level shift coordinates using a statistical technique as shown in equations (8) and (9). The master image and the slave images are aligned based on the net shift value. Here the mean is taken over the number of anchor points. Net _ shift _ x = mean fine _ shift _ x + mean coarse _ shift _ x Net _ shift _ y = mean fine _ shift _ y + mean coarse _ shift _ y

[0051] At step 328 of the method 300, the interferogram generation module 244 executed by the one or more hardware processors 102 is configured by the programmed instructions to generate an interferogram based on the master image and a complex conjugate of the slave image using an interferogram generation technique. For example, the interferogram generation technique multiples the master image and the complex conjugate of the slave image thus subtracting the phase. At step 330 of the method 300, the deformation profiling module 246 executed by the one or more hardware processors 102 is configured by the programmed instructions to profile a deformation pertaining to the surface under test based on color variations in the interferogram using an interferogram interpretation technique. For example, in the interferogram, the region with uniform phase represents a planar surface. This will have a single colormap representation in the interferogram. In the zone having wear, the color would change.Experimentation details:

[0052] In an embodiment, the present disclosure is experimented as follows. The experimental setup is described as follows: The radar sensor was mounted on a conveyor belt and was made to perform stripmap scan linearly (at 3cm / s speed). The 60GHz (with 7GHz bandwidth,range resolution 0.5mm) pulse coherent Acconeer XM112 low-power monostatic radar was used in the present disclosure. Unlike other COTS mm-wave radars which do not provide coherent phase measurement, this radar preserves the phase information. Additionally, it is provided with a hyperbolic dielectric lens which can narrow the wide beamwidth of the radar to a comparatively narrow and focused beam has 17° and 15° along the E-plane and H-plane respectively. The sample under test (SuT) consists of two aluminum alloy plates, among which one plate has been hammered to create worn out zones. The radar partially illuminates the sample to cover a 10 cm wide swath. The first pass of the radar was taken with a normal sample while during the repeat pass, the sample had already been replaced with a worn-out sample.

[0053] In an embodiment, the amplitude of the SAR images of the normal sample reconstructed using the present disclosure shows uniform backscatter throughout the stretch of the object. It is observed that the dimension in the range comes out to be 10cm (i.e., theoretical swath width) calculation and the azimuth dimension matches to that of the real sample.

[0054] It is observed that, the phase difference has remained constant throughout the illuminated area except the part where the sample was hammered. The deformation of the marked points with respect to the reference point (non-deformed) is evaluated using equations (1) through (5) and summarized in Table I. Now referring to Table I, the table enlists various sampling points from the interferogram, the ground truth deformation taken using a laser rangefinder equipment, estimating the unwrapped phase difference and wear and finally, the squared error between the estimated wear and the ground truth deformation. Table ISl. No of the markingGround truth (mm)Unwrapped (rad)Measured wear (mm)Squared error10.51.090.430.028921.52.451.220.078430.80.530.530.729400.610.240.0576500.660.260.0676MSE0.0611

[0055] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments.

[0056] The embodiments of present disclosure herein address the unresolved problem of surface wear inspection using millimeter wave radar. The present disclosure conducts repeat-pass SAR interferometry in short range for non-contact surface profiling use-cases like in-situ surface wear inside mills, surface defect inspection through packaging or similar occluded environments. Further, the present disclosure implemented an unique variable aperture range-doppler algorithm to generate SAR images and an anchor point-based automated co-registration scheme to align these images for InSAR. The amplitude images of the worn-out sample provide an indication of the worn-out zone. Furthermore, the interferogram gives us a sub-mm precise measure of the deformation caused due to wear. The mean squared error of wear quantification is found to be 0.0611.

Claims

1. A processor implemented method (300) for surface wear inspection of metallic surfaces using a short range Interferometric Synthetic Aperture Radar, InSAR, technique, the method comprising: receiving (302), by one or more hardware processors, a plurality of uncompressed raw Synthetic Aperture Radar, SAR, images pertaining to a surface under test, wherein the plurality of uncompressed raw SAR images comprises a first plurality of raw SAR images captured sequentially from a plurality of vantage points in a single pass and a second plurality of raw SAR images captured sequentially from the plurality of vantage points in a repeat pass; generating (304), by the one or more hardware processors, a plurality of reconstructed SAR images based on the plurality of uncompressed raw SAR images using a variable focusing based Range Doppler Algorithm, RDA, wherein the step of generating the plurality of reconstructed SAR images from the plurality of uncompressed raw SAR images using the variable focusing based RDA comprises: generating a plurality of range compressed SAR images based on the plurality of uncompressed raw SAR images using a range reference function, wherein a plurality of signals returned from a single point are compressed into a single pixel; finding a target range corresponding to each of the plurality of range compressed SAR images using the reference function, wherein the target range represents a range where the range correction is required; generating a plurality of anchor points corresponding to each of a plurality of target ranges based on a pulse-width at mid-reference level of a plotted range and a corresponding azimuth waveform using the spread function; creating a plurality of windows in range direction based on the plurality of anchor points using the signal processing technique; computing a plurality of window parameters for each of the plurality of windows in each range direction using the range reference function, wherein the plurality of parameters comprises a Doppler Centroid value, a Doppler frequency value and a synthetic aperture length; selecting a plurality of auto-focused SAR images by computing a corrected focus magnitude corresponding to each of the plurality of windows based on the plurality of window parameters using an automatic focus enhancement technique; generating a plurality of high-resolution SAR images by performing an Azimuth match filtering followed by an Inverse Fourier Transform on the plurality of auto-focused SAR images; and generating the plurality of reconstructed SAR images based on the plurality of high-resolution SAR image using a ground range interpolation; and examining, by the one or more hardware processors, each of the plurality of reconstructed SAR images by iteratively performing: selecting (306) a master image from a first plurality of reconstructed SAR images and a slave image from a second plurality of reconstructed SAR images, from among the plurality of reconstructed SAR images; assigning (308) a plurality of anchor points for the master image and a plurality of anchor points for the slave image, using a signal processing technique, wherein the plurality of anchor points are generated using a spread function; computing (310) a plurality of coarse level shift coordinates based on a difference between the plurality of anchor points corresponding to the master image and the plurality of anchor points corresponding to the slave image; generating (312) a plurality of pixel groups for the master image and a plurality of pixel groups for the slave image by extracting a pixel group surrounding each of the plurality of anchor points corresponding to the master image and the slave image using a pixel extraction technique; generating (314) a frequency domain representation of the plurality of pixel groups for the master image and a frequency domain representation of the plurality of pixel groups of the slave image by computing a 2D Fast Fourier Transform, FFT, on the corresponding plurality of pixel groups; obtaining (316) a plurality of over-sampled pixel groups for the master image and a plurality of over-sampled pixel groups for the slave image based on the corresponding frequency domain representation using zero padding; generating (318) a spatial domain representation of the plurality of over-sampled pixel groups for the master image and a spatial domain representation of the plurality of over-sampled pixel groups for the slave image by computing an Inverse FFT, IFFT, on the plurality of over-sampled pixel groups corresponding to the master image and the slave image; identifying (320) a deformation information associated with each of the plurality of pixel groups for the master image and a deformation information associated with each of the plurality of pixel groups for the slave image based on the corresponding spatial domain representation using a spatial data analysis technique; computing (324) a plurality of fine level shift coordinates between the master image and the slave image based on corresponding 2D cross-correlated pixel groups using a sub-pixel level spatial shift computation technique; estimating (326) a net shift value based on the plurality of coarse level shift coordinates and the plurality of fine level shift coordinates using a statistical technique, wherein the master image and the slave image are aligned using the net shift value; generating (328) an interferogram based on the master image and a complex conjugate of the slave image using an interferogram generation technique; and profiling (330) a deformation pertaining to the surface under test based on color variations in the interferogram using an interferogram interpretation technique.

2. The method as claimed in claim 1, wherein the plurality of anchor points comprises a near-range, a far-range, a near-azimuth, and a far-azimuth.

3. The method as claimed in claim 1, wherein the single pass captures the first plurality of uncompressed raw SAR images from the plurality of vantage points at a time, and the repeat pass captures the second plurality of uncompressed raw SAR images from the plurality of vantage points at a varying time.

4. A system (100) for surface wear inspection of metallic surfaces using a short range Interferometric Synthetic Aperture Radar, InSAR, technique, the system comprising: at least one memory (104) storing programmed instructions; one or more Input / Output (I / O) interfaces (112); and one or more hardware processors (102) operatively coupled to the at least one memory (104), wherein the one or more hardware processors (102) are configured by the programmed instructions to: receive a plurality of uncompressed raw Synthetic Aperture Radar, SAR, images pertaining to a surface under test, wherein the plurality of uncompressed raw SAR images comprises a first plurality of raw SAR images captured sequentially from a plurality of vantage points in a single pass and a second plurality of raw SAR images captured sequentially from the plurality of vantage points in a repeat pass; generate a plurality of reconstructed SAR images based on the plurality of uncompressed raw SAR images using a variable focusing based Range Doppler Algorithm, RDA, wherein generating the plurality of reconstructed SAR images from the plurality of uncompressed raw SAR images using the variable focusing based RDA comprises: generating a plurality of range compressed SAR images based on the plurality of uncompressed raw SAR images using a range reference function, wherein a plurality of signals returned from a single point are compressed into a single pixel; finding a target range corresponding to each of the plurality of range compressed SAR images using the reference function, wherein the target range represents a range where the range correction is required; generating a plurality of anchor points corresponding to each of a plurality of target ranges based on a pulse-width at mid-reference level of a plotted range and a corresponding azimuth waveform using the spread function; creating a plurality of windows in range direction based on the plurality of anchor points using the signal processing technique; computing a plurality of window parameters for each of the plurality of windows in each range direction using the range reference function, wherein the plurality of parameters comprises a Doppler Centroid value, a Doppler frequency value and a synthetic aperture length; selecting a plurality of auto-focused SAR images by computing a corrected focus magnitude corresponding to each of the plurality of windows based on the plurality of window parameters using an automatic focus enhancement technique; generating a plurality of high-resolution SAR images by performing an Azimuth match filtering followed by an Inverse Fourier Transform on the plurality of auto-focused SAR images; and generating the plurality of reconstructed SAR images based on the plurality of high-resolution SAR image using a ground range interpolation; and examine each of the plurality of reconstructed SAR images by iteratively performing: selecting a master image from a first plurality of reconstructed SAR images and a slave image from a second plurality of reconstructed SAR images, from among the plurality of reconstructed SAR images; assigning a plurality of anchor points for the master image and a plurality of anchor points for the slave image, using a signal processing technique, wherein the plurality of anchor points are generated using a spread function; computing a plurality of coarse level shift coordinates based on a difference between the plurality of anchor points corresponding to the master image and the plurality of anchor points corresponding to the slave image; generating a plurality of pixel groups for the master image and a plurality of pixel groups for the slave image by extracting a pixel group surrounding each of the plurality of anchor points corresponding to the master image and the slave image using a pixel extraction technique; generating a frequency domain representation of the plurality of pixel groups for the master image and a frequency domain representation of the plurality of pixel groups of the slave image by computing a 2D Fast Fourier Transform, FFT, on the corresponding plurality of pixel groups; obtaining a plurality of over-sampled pixel groups for the master image and a plurality of over-sampled pixel groups for the slave image based on the corresponding frequency domain representation using zero padding; generating a spatial domain representation of the plurality of over-sampled pixel groups for the master image and a spatial domain representation of the plurality of over-sampled pixel groups for the slave image by computing an Inverse FFT, IFFT, on the plurality of over-sampled pixel groups corresponding to the master image and the slave image; identifying a deformation information associated with each of the plurality of pixel groups for the master image and a deformation information associated with each of the plurality of pixel groups for the slave image based on the corresponding spatial domain representation using a spatial data analysis technique; computing a plurality of fine level shift coordinates between the master image and the slave image based on corresponding 2D cross-correlated pixel groups using a sub-pixel level spatial shift computation technique; estimating a net shift value based on the plurality of coarse level shift coordinates and the plurality of fine level shift coordinates using a statistical technique, wherein the master image and the slave image are aligned using the net shift value; generating an interferogram based on the master image and a complex conjugate of the slave image using an interferogram generation technique; and profiling a deformation pertaining to the surface under test based on color variations in the interferogram using an interferogram interpretation technique.

5. The system of claim 4, wherein the plurality of anchor points comprises a near-range, a far-range, a near-azimuth, and a far-azimuth.

6. The system of claim 4, wherein the single pass captures the first plurality of uncompressed raw SAR images from the plurality of vantage points at a time, and the repeat pass captures the second plurality of uncompressed raw SAR images from the plurality of vantage points at a varying time.

7. A computer program product including a non-transitory computer-readable medium having embodied therein a computer program for surface wear inspection of metallic surfaces using a short range Interferometric Synthetic Aperture Radar, InSAR, technique, which when executed on a computing device, causes the computing device to carry out the method of claims 1-3.

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